供應鏈網路設計在供應鏈管理中占有很重要的地位，其目的在於提供供應鏈管理一個發揮效應的工作平台。供應鏈網路設計若依規劃時間的長短，可區分為戰術、戰略及操作等不同層級，其決策內容分別為設施定址，存貨及運輸規劃。以策略層級而言，因為投入的資源較大，決定後就不易改變，故一般多運用單期模式。以戰術層級而言，其目的係運用較近期的需求資訊，可依顧客的需求，劃分多期，求解最佳的運送及存貨規劃。由於供應鏈網路設計決策的時間軸不同，很難以同一模式建構完整的供應鏈網路，故本論文發展兩階層求解方法，第一階層為策略階段，求出最適的設施定址。第二階段為戰術階段，續依據策略階段的定址決策，發展最適的存貨與運送規劃。另基於策略階段決策，影響日後系統運作深遠，故本論文以不同的假設，設計三個不同的策略模式，因為本論文所探討的問題涵括了定址-存貨、存貨-路徑整合問題及路徑等問題，均屬困難性問題，囿於精確求解法只限求小、中型的問題，所以在本論文採用以遺傳演算法為基石求解上述所陳問題。

Supply chain network design (SCND) which provides an optimal configuration of platform is an important task in supply chain management. The SCND can be classified as strategic, tactical and operational level decisions depending on the time horizons, those decisions include the number, location of facilities, inventory policy and transportation plan. The strategic decision requires the vast investment; the decision is expected to operate for a long term. Therefore, it is reasonable to use single-period model. In the tactical and operational level, the demand information is more accurately, therefore, the decision maker needs a short-term inventory and transportation planning which generate the product flows in the network. In this dissertation, we attempt to integrate a strategic and tactical plan and develop two stage models to construct the optimal SCND. In first stage, three strategic models are presented according to the various scenarios. In second stage, we propose a tactical model which is multi period inventory routing model for determining the time and amount of goods to deliver. Because these models consider location-inventory and inventory-routing problem, both belong to NP-hard problem, exact methods only can solve small or medium size problem, therefore a various genetic algorithms base heuristic are proposed the solve these problems.

Index
List of Tables	VI
List of Figures	VII
Chapter 1.Introduction	1
1.1.	Research background	1
1.1.1. Supply chain environment	1
1.1.2. Physically construction	3
1.1.3. DC implementation	4
1.2.	Objective & Motivation	5
1.3.	Research scope	6
1.3.1. Supply chain network configuration	6
1.3.2. Research methodology	7
1.4.	Organization of the dissertation	7
Chapter 2.Literature Review	10
2.1. Location - Inventory models	11
2.2. Location - Routing models	14
2.3. Inventory - Routing models	17
2.4. Dual sale channel SCN	20
2.5. Multiple Criteria Decision Making (MCDM)	23
2.6. Research problem	26
Chapter 3 Design an integrated strategic and tactical SCND model	32
3.1. Introduction	32
3.2. The strategic stage	33
3.2.1. Inventory control decision	34
3.2.2. Transportation decision	36
3.3. The tactical stage	38
3.4. Solution methodologies	41
Chapter 4 Strategic planning - baseline model	45
4.1. Introduction	45
4.2. Problem statement	46
4.3. Model formulation and solution methodologies	49
4.3.1. Notation	49
4.3.2. Model formulation	50
4.3.3. Solution methodologies	53
4.3.3.1. Big clients allocation phase	55
4.3.3.2. Small clients allocation phase	56
4.3.3.3. Small clients routing phase	57
4.4. Experiment results	58
4.4.1. Evaluation instances	58
4.4.2. Computational results on the problem instances	60
4.5. Conclusion	64
Chapter 5. Strategic planning- demand depend model	66
5.1. Introduction	66
5.2. Problem statement	66
5.3. Model formulation and solution methodologies	67
5.4. Numerical examples and sensitivity analysis	72
5.5. Conclusion	78
Chapter 6. Strategic planning multi-objective model	79
6.1. Introduction	79
6.2. Problem statement	82
6.3. Mathematical model and formulation	84
6.3.1. Proposed heuristic procedures	90
6.3.2. NSGA-II for Pareto solutions	90
6.3.3. TOPSIS and Shannon entropy for the best compromise solution	92
6.4. Numerical experiment	94
6.4.1. Data Generation	94
6.4.2. Computational results	95
6.4.3. Sensitivity analysis	99
6.5. Conclusion	103
Chapter 7. Tactical planning DIRT model	105
7.1. Introduction	105
7.2. Problem statement	108
7.3. Model formulation and solution methodologies	111
7.3.1. Model formulation	111
7.3.2. Solution methodologies	113
7.3.2.1. Representation and initiation	114
7.3.2.2. Replenishment plan with capacity limitations	116
7.3.2.3. Lateral transshipment	116
7.3.2.4. Redistribution	117
7.3.2.5. Crossover operator	121
7.3.2.6. Mutation operator	123
7.4. Experiment	123
7.4.1. Experimental design	123
7.4.2. Computational results	124
7.5. Conclusion	130
Chapter 8.Conclusion and future work	131
8.1. Summary and research findings	131
8.2. Major contributions	132
8.3. Limitations of the research	134
8.4. Recommendations for future research	134
References	138
Appendix: Abbreviation table	156
 
List of Tables
Table 2.1 IRP variants structure	20
Table 2.2 A comparison for the proposed models and literature’s models	31
Table 3.1 The comparison between the syrategic and tactical planning	33
Table 3.2 The comparison for three strategic models	38
Table 3.3 Study key issues	40
Table 4.1 The model parameters for problem instances	59
Table 4.2 Computational results for P_50_300_T1_S2	60
Table 4.3 Computational results for nine problem instances	61
Table 5.1 Model parameters configuration for problem instances	73
Table 5.2 Parameters value for problem instances	74
Table 6.1 Non-dominated solution set from NSGA-II	97
Table 6.2 Computational results incurred from TOPSIS	98
Table 6.3 Computational results of 5 top-ranking solutions for P25_500 	100
Table 6.4 Computational results of 5 top-ranking solutions for P25_800	101
Table 6.5 Computational results of 5 top-ranking solutions for P25_1000	102
Table 7.1 The cost components of the DIRPTR model	125
Table 7.2 The cost components of the IRPT-OU model	126
Table 7.3 The cost components of the IRP model	127
Table 7.4 The stock level in the DIRPTR model for D10T10 instance	128
Table 7.5 The stock level in the IRPT-OU model for D10T10 instance	128
Table 7.6 The stock level in the traditional IRP model for D10T10 instance	129
 
List of Figures
Figure 1.1 SCM activities	2
Figure 1.2 The VMI supply chain structure	3
Figure 1.3 The supply chain network	4
Figure 1.4 Supply chain model	6
Figure 1.5 The research framework	9
Figure 2.1 Classification of DRAI problems	19
Figure 3.1 SCM decision scheme	33
Figure 3.2 The process of the strategic models	34
Figure 3.3 Three-layer SCND order scheme	35
Figure 3.4 Two-echelon SCND distribution scheme	37
Figure 3.5 The process of tactical model	39
Figure 3.6 The tactical model distribution scheme	40
Figure 3.7 The strategic and tactical scheme	41
Figure 3.8 The general GA flow chart	44
Figure 4.1 The dual channel SCND structure	47
Figure 4.2 Flowchart of proposed heuristic method	55
Figure 4.3 The small clients cluster procedure	57
Figure 4.4 Small clients vehicle routing procedure	58
Figure 4.5 Trade-off trends among costs in P_50_300_T1_S2 problem instance	61
Figure 4.6 Number of open DCs under various cost scenarios	62
Figure 4.7 The P_50_300_T1_S1 problem instance convergence trends	64
Figure 4.8 The diversity evolution for P_50_300_T1_S1 problem instance	64
Figure 5.1 The customers channel preference rate impact of number of open DC	76
Figure 5.2 The optimal open DCs number for various problem instance	76
Figure 5.3 The cost components for customers channel preference rate in case1	76
Figure 5.4 The cost components for customers channel preference rate in case2	77
Figure 5.5 The cost components for customers channel preference rate in case3	77
Figure 5.6 The cost components for customers channel preference rate in case4	77
Figure 5.7 The cost components for customers channel preference rate in case5	78
Figure 6.1 The flow chart of proposed heuristic procedure	89
Figure 6.2 The NSGA-II solution scheme	91
Figure 6.3 The Pareto-fronts for the base line instances	99
Figure 6.4 The number of open DCs for various problem top ranking	103
Figure 7.1 The distribution scheme of the DIRPTR model	109
Figure 7.2 The system event of DIRPTR model	111
Figure 7.3 Two-dimensional genetic solutions	115
Figure 7.4 The replenishment scheme with capacity limitation	116
Figure 7.5 The lateral transshipment algorithm	118
Figure 7.6 The modified replenishment and transshipment solution process	120
Figure 7.7 The vehicle scheduling	121
Figure 7.8 The crossover operator vehicle scheduling	122
Figure 7.9 The mutation operator vehicle scheduling	123
Figure 7.10 The models performance for the D10T10 instance	129
Figure 7.11 The DIRPTR cost component for the D10T10 instance	130

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